One can begin to think about what functions and features are expected of a smart learning environment? From our knowledge in the previous module, a key motivation for designing SLE to support learning and teaching is connected to the kind of features that improves the user experience. Experts have expressed what makes a system smart in the context of education . For example, a learning environment can be considered smart when it makes use of adaptive technologies or when it is designed to include innovative features and capabilities that improve understanding and performance. In a general sense, a smart learning environment is one that is "effective, efficient and engaging” (Spector, 2014, p.2).

Sirkka Freigang  described a smart learning environment to constitute the features represented in the figure below




Technical Features of SLE

  1. Tracking learning process.  The SLE can track the learning status of learners to enable a more accurate support for learning.
  2. Recognizing learning scenario.  Personalized resources will be identified according to the learning event, which includes the learning time, place, peers and activities.
  3. Awareness of the physical environment . The air, temperature, light, sound, and smell will be monitored and adjusted to accommodate learners' needs.
  4. Connecting learning community. Learners will be connected to learning communities to enrich the learning experience

 Hwang (2014) criteria for a smart learning environment:

  • Detect and take into account the real-world contexts.
  • Situate learners in real-world scenarios.
  • Adapt learning interfaces for individual learners.
  • Adapt learning tasks for individual learners.
  • Provide personalized feedback or guidance.
  • Provide learning guidance or support across disciplines.
  • Provide learning guidance or support across contexts.
  • Recommend learning tools or strategies.
  • Consider learners' online learning status.
  • Consider learners' real-world learning status.
  • Facilitate both formal and informal learning.
  • Take multiple personal and environmental factors into account.
  • Interact with users via multiple channels.
  • Provide learners with support in advance, across real and virtual contexts


Broad classification of the characteristics of SLE

1. Adaptivity
Adaptivity is the state or quality of being adaptive. A smart learning environment has the capacity to adapt to different contextual scenarios. For instance, a learning environment is smart if it is capable of adapting instructional process on different instructional parameters such as sequence of tasks, task difficulty, time, type of feedback, pace or learning speed, reinforcement plan, etc. The adaptations of the instructional process to the individual have been investigated from different research perspectives, for example, research in learner modeling, intelligent tutoring systems, adaptive hypermedia, adaptive instructional designs and others.

For instance, a tool designed with adaptive sequence has a lot going on behind the scenes. This tool is continuously collecting and analyzing student data to automatically change what a student sees next; from the order of skills a student works on, to the type of content a student receives. Example of an algorithm for an adaptive sequence in a learning environment used by John and Mary is presented below.



For further reading on adaptivity,  see  https://moodle.uef.fi/mod/folder/view.php?id=1690544

2. Personalization

According to the US Department of Education, Office of Educational Technology, personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. Learning objectives, instructional approaches, and instructional content (and its sequencing) may all vary based on learner needs. In addition, learning activities are made available that are meaningful and relevant to learners, driven by their interests and often self-initiated.

Hence, in a personalized learning environment,

  1. The pace of learning is adjusted
  2. Learning objectives, approaches, content, and tools are tailored and optimized for each learner
  3. Learning is driven by learner interests
  4. Learners are given choice in what, how, when, and where they learn
  5. Learning is often supported by technology

For examples, WileyPLUS, introduced in the embedded  video shows how a learning environment personalizes  learning

 

 


3. Context and Location-awareness

According to Hwang (2014), a smart learning environment is context-aware; that is, the learner’s situation or the contexts of the real-world environment in which the learner is located are sensed, implying that the system is able to provide learning support based on the learner’s online and real-world status. As presented in the table, a smart learning environment is able to identify, recognize, understand and become aware of the phenomenon, event, object, impact, etc of learners context such as lights, temperature, humidity, sound, etc. Read this article provided on context-awareness for more understanding of the concept.


SLE characteristics, technologies, and smartness activities (Adapted from Uskov et al., 2015)

SLE characteristics

Tools, agents, technologies involved

Smart Activities

Adaptive:
Ability to modify physical or behavioral characteristics to fit the environment or better survive in it. 

  • Web technologies
  • Session-based analytics
  • Personal digital devices
  •  VR and AR systems
  • Presentation technologies (Smartboards, etc) 
  • Social media
  • Sensors (air, temperature, number of persons, participation roles, ….)

  • Communicate (local & remote)
  • Share content
  • View content in a preferred language
  • Initiate session with voice/facial/gesture commands
  • Ask questions
  • Present (local & remote)
  • Discuss
  • Annotate

Sensing:
Ability to identify, recognize, understand and/or become aware of phenomenon, event, object, impact, etc.

  • Triggers actions, defined in assorted models (learner, school, teacher, Smart Classroom, etc.)
  • Big Data
  • Multiple interfaces and channels keyboard, screen, voice, agent, eye movements, gestures, etc

  • Automatic adjustment of classroom environment (lights, AC, temperature, humidity, etc.)
  • Real-time collection of student feedback from diverse contexts
  • Monitoring student activity
  • Process real-time classroom data
  • Deliver custom support and scaffolding for special needs students
  • Support agent-based systems
  • Interact with smart systems
  • Connect multi-location students

Inferring:
Ability to make logical conclusion(s) on the basis of raw data, processed information, observations, evidence, assumptions, rules and logic reasoning. stand and/or become aware of phenomenon, event, object, impact, etc.

  • Simple rule-based process engines
  • More complex inference engines
  • Natural language processors

  • Recognize every individual
  • Process real-time classroom data
  • Process incomplete classroom datasets
  • Discuss presented learning content and assignments with remote students in real-time and using preferred language by each student

Learning/knowledge acquisition:
Ability to acquire new or modify existing knowledge, experience, behavior to improve performance, effectiveness, skills, etc.

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

  • Ability to suggest changes to the system
  • Real-time skills assessment
  • Real-time knowledge assessment
  • Accommodate and enact multiple intelligences

Anticipating:
 The ability of thinking or reasoning to predict what is going to happen or what to do next.

  • Predictive engine

(predictive analytics)

·         All the above


References

Spector, J. M. (2014). Conceptualizing the emerging field of smart learning environments. Smart learning environments, 1(1), 2.

https://blog.bosch-si.com/future-of-work/iot-in-education-by-designing-smart-learning-environments/

Hwang, G. J. (2014). Definition, framework and research issues of smart learning environments-a context-aware ubiquitous learning perspective. Smart Learning Environments, 1(1), 4.

Uskov, V. L., Howlett, R. J., & Jain, L. C. (Eds.). (2015). Smart education and smart e-learning (Vol. 41). Springer.



 


Última modificación: viernes, 28 de febrero de 2020, 15:40